This is a list of all of my publications so far (last updated December 2023):

1. Rob Churchill and Lisa Singh. Using topic-noise models to generate domain-specific topics across data sources. In Knowledge and Information Systems (KAIS), 2023. (pdf)
2. Rob Churchill and Lisa Singh. Temporal Topic-Noise Models for Social Media Data Sets. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2022. (pdf)
3. Rob Churchill, Lisa Singh, Rebecca Ryan, and Pamela Davis-Kean. A Guided Topic Model for Social Media Data. In The Web Conference (WWW), 2022. (pdf)
4. (Doctoral Thesis) Rob Churchill. Modernizing Topic Models: Accounting for Noise, Time, and Domain Knowledge. Diss. Georgetown University, 2021. (pdf)
5. Jaren Haber, Lisa Singh, Ceren Budak, Josh Pasek, Meena Balan, Ryan Callahan, Rob Churchill, Brandon Herren, Kornraphop Kawintiranon. Research Note: Lies and presidential debates: How political misinformation spread across media streams during the 2020 election. Harvard Kennedy School Misinformation Review, 2021. (html)
6. Rob Churchill and Lisa Singh. Topic-Noise Models: Modeling Topic and Noise Distributions in Social Media Post Collections. In International Conference on Data Mining (ICDM), 2021. (pdf)
7. Rob Churchill and Lisa Singh. The evolution of topic modeling. In ACM Computing Surveys (CSUR), 2021. (pdf)
8. Rob Churchill and Lisa Singh. textPrep: A text preprocessing toolkit for topic modeling on social media data. In The DATA Conference, 2021. (pdf)
9. Rob Churchill and Lisa Singh. Percolation-based topic modeling for tweets. In KDD Workshop on Issues of Sentiment Discovery and Opinion Mining (WISDOM), 2020. (pdf)
10. Rob Churchill, Lisa Singh, and Josh Pasek. The impact of pre-processing classes on meaningful topics from online text data. In MIDAS Symposium, 2018.
11. Rob Churchill, Lisa Singh, and Christo Kirov. A temporal topic model for noisy mediums. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2018. (pdf)